ArticleScientific reports2013
FOGSAA: Fast Optimal Global Sequence Alignment Algorithm.
Article in Scientific reports, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Computational biology and artificial intelligence in mRNA vaccine design for cancer immunotherapy.Frontiers in cellular and infection microbiology · 2024Review
- A novel model for protein sequence similarity analysis based on spectral radius.Journal of theoretical biology · 2018Article
- A systematic comparison of error correction enzymes by next-generation sequencing.Nucleic acids research · 2017Article
- Protein Sequence Comparison Based on Physicochemical Properties and the Position-Feature Energy Matrix.Scientific reports · 2017Article
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2 authors.
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Abstract
In this article we propose a Fast Optimal Global Sequence Alignment Algorithm, FOGSAA, which aligns a pair of nucleotide/protein sequences faster than any optimal global alignment method including the widely used Needleman-Wunsch (NW) algorithm. FOGSAA is applicable for all types of sequences, with any scoring scheme, and with or without affine gap penalty. Compared to NW, FOGSAA achieves a time gain of (70-90)% for highly similar nucleotide sequences (> 80% similarity), and (54-70)% for sequences having (30-80)% similarity. For other sequences, it terminates with an approximate score. For protein sequences, the average time gain is between (25-40)%. Compared to three heuristic global alignment methods, the quality of alignment is improved by about 23%-53%. FOGSAA is, in general, suitable for aligning any two sequences defined over a finite alphabet set, where the quality of the global alignment is of supreme importance.
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